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This study aimed to establish quality criteria to assist patients, caregivers, and the public in evaluating the reliability of online health information.
While ChatGPT has gained popularity in various domains, it may not be the ideal focus for medical professionals due to its reliance on language pattern prediction rather than direct fact retrieval, potentially leading to inaccurate outputs. We emphasize the limitations of ChatGPT's training data, which mainly come from non-specialized sources and may result in misleading answers in highly specialized medical domains. We advocate for a shift towards specialized medical large language models (LLMs) that are trained using authoritative medical databases, supplemented by human validation, to ensure accuracy and completeness of data.
How to optimize the systematic review process using AI tools
Nicholas Fabiano, Arnav Gupta, Nishaant Bhambra, Brandon Luu, Stanley Wong, Muhammad Maaz, Jess G. Fiedorowicz … See all authors
First published: 23 April 2024
https://doi.org/10.1002/jcv2.12234
Nicholas Fabiano, Arnav Gupta and Nishaant Bhambra contributed equally to this paper.
We introduce Get Free Copy (https://getfreecopy.com), a web-based platform designed to streamline the search for biomedical literature across major repositories like arXiv, bioRxiv, medRxiv, and PubMed Central (PMC). Addressing challenges posed by paywalls and fragmented databases, it offers a unified interface for efficient retrieval of free, legitimate copies of biomedical literature. The platform's implementation involves a Node.js backend and dynamic front-end display, enhancing accessibility and research efficiency. As an open-source project, Get Free Copy represents a significant contribution to the open-access movement, inviting global researcher collaboration for further development.